
\begin{landscape}\begin{table}[!h]
\centering
\caption{Government Framing of Countries Conditioning on Regime by Standard Error Type\label{tab:polityse}}
\centering
\resizebox{\ifdim\width>\linewidth\linewidth\else\width\fi}{!}{
\begin{threeparttable}
\begin{tabular}[t]{lllllllllll}
\toprule
\multicolumn{1}{c}{\em{ }} & \multicolumn{10}{c}{\em{Similarity with Country Dictionary (\%)}} \\
\multicolumn{1}{c}{ } & \multicolumn{5}{c}{Chaos} & \multicolumn{5}{c}{Corruption} \\
\cmidrule(l{3pt}r{3pt}){2-6} \cmidrule(l{3pt}r{3pt}){7-11}
  & Hetero. (1) & Cluster (Obj) (2) & Boot (Obj) (3) & Cluster (Attr) (4) & Boot (Attr) (5) & Hetero. (6) & Cluster (Obj) (7) & Boot (Obj) (8) & Cluster (Attr) (9) & Boot (Attr) (10)\\
\midrule
Government Accounts & -0.11 & -0.11 & -0.11 & -0.11 & -0.11 & -7.18*** & -7.18*** & -7.18*** & -7.18*** & -7.18***\\
 & (0.29) & (1.05) & (1.03) & (1.15) & (1.14) & (0.40) & (0.98) & (0.93) & (1.59) & (1.58)\\
Democracy (vdem) & -0.42 & -0.42 & -0.42 & -0.42 & -0.42 & 1.40 & 1.40 & 1.40 & 1.40 & 1.40\\
 & (1.73) & (7.71) & (7.39) & (1.00) & (1.03) & (2.41) & (8.70) & (8.57) & (1.70) & (1.63)\\
Govt. Acct. x Democracy & 2.79*** & 2.79*** & 2.79*** & 2.79*** & 2.79*** & 3.86*** & 3.86*** & 3.86*** & 3.86*** & 3.86***\\
 & (0.26) & (0.96) & (0.98) & (0.34) & (0.33) & (0.37) & (1.02) & (1.01) & (0.38) & (0.38)\\
ln Frequency 1 & -1.58*** & -1.58*** & -1.58*** & -1.58*** & -1.58*** & -1.96*** & -1.96*** & -1.96*** & -1.96*** & -1.96***\\
 & (0.03) & (0.16) & (0.16) & (0.07) & (0.06) & (0.04) & (0.16) & (0.17) & (0.11) & (0.11)\\
ln Frequency 2 & -3.57*** & -3.57*** & -3.57*** & -3.57*** & -3.57*** & -2.31*** & -2.31*** & -2.31*** & -2.31*** & -2.31***\\
 & (0.04) & (0.09) & (0.09) & (0.37) & (0.36) & (0.05) & (0.06) & (0.06) & (0.44) & (0.43)\\
\midrule
\addlinespace[]
\multicolumn{11}{l}{\textit{Statistics}}\\
\hspace{1em}Observations & 93453 & 93453 & 93453 & 93453 & 93453 & 48636 & 48636 & 48636 & 48636 & 48636\\
\addlinespace[]
\multicolumn{11}{l}{\textit{Fixed effects}}\\
\hspace{1em}Subscription Account & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\
\hspace{1em}Year & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\
\hspace{1em}Dictionary FE & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\
\bottomrule
\end{tabular}
\begin{tablenotes}
\item Notes: $^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01 Regressions labeled with ``Boot" use Wild Bootstrapping to compute standard errors. Dependent variable is cosine similarity between country and respective attribute dictionaries. Controls for dictionary term frequency are shown. Unit of analysis is the object-attribute word pair, which varies according to the object and attribute dictionary size.
\end{tablenotes}
\end{threeparttable}}
\end{table}
\end{landscape}
